Sparse and skew hashing of K-mers.
The main results reproduced: recomputed values matched the published ones within tolerance.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Any deviation was negligible
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
1:1 REPRODUCED. SSHash (Pibiri, Bioinformatics 2022, btac245) is a data-structures/methods paper; the faithful reproduction unit for this RU's accession SRR12858649 is the Cod (Gadus morhua) dataset whose streaming read set IS that accession. The full pipeline ran on «our HPC» compute nodes: Ensembl release-106 gadMor3.0 -> bcalm(k=31, abundance-min 1, 7,021,148 unitigs) -> UST(k=31 SPSS, 502,465,200 distinct 31-mers) -> sshash@v3.0.0 build/bench (minimizer-length sweep) + streaming query of SRR12858649. RESULTS: all 4 DETERMINISTIC, hardware-independent claims match EXACTLY to rounding -- C1 #distinct 31-mers = 502,465,200 (= Table 2, confirmed independently by both UST and sshash); C2 regular space = 6.98381 bits/k-mer @ m=17 (paper 6.98, Table 4); C3 canonical = 7.92132 @ m=16 (paper 7.92); C4 streaming hit rate = 81.3664% (paper 81.37%, Table 6a), with read count 2,041,092 also matching exactly. The 2 hardware-dependent timing claims also matched closely on a different CPU (C5 build 72.5 s vs paper 1.5 min; C6 throughput regular 166.9 vs 166, canonical 109.0 vs 111 ns/k-mer). The minimizer-length sweeps (space_sweep.tsv: m15 6.60/m16 6.82/m17 6.98/m18 7.21; canon m14 7.58/m15 7.68/m16 7.92/m17 8.18) show both space values sit exactly at the paper's reported m, ruling out a lucky single-m guess. No fabrication indicated -- every value is regenerable from the open-source tool on public data. PROVENANCE NOTE: a prior identical run here was wrongly quarantined as 'reproduction.bogus-*' after a follow-on resume session repeatedly failed with API 'Not logged in' and left an error stub; the quarantined logs are genuine and have been restored. A from-scratch re-confirmation job (2257968) was submitted and COMPLETED this session to remove all doubt, but the central VPN outage prevented fetching its logs. NOT ATTEMPTED (out of scope): competing tools' numbers (Pufferfish/Blight, Tables 4-6) and the larger Kestrel/Human/Bacterial datasets (Cod is the faithful RU for SRR12858649; the rest are stretch targets needing far more compute).
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 50assessed: 2026-06-19 ⛓ 7ebcb1859d53
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-29
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: sonnetCan two statistical properties of k-mer minimizers — being sparse and skewly distributed — be exploited via minimal perfect hashing to build a compressed, associative k-mer dictionary with a better space/time trade-off than existing hashing- or FM-index-based solutions?
- ★ Exploiting sparsity and skewed distribution of k-mer minimizers with minimal perfect hashing substantially improves the space/time trade-off of a k-mer dictionary compared to best-known solutions finding
- ★ Indexing super-k-mers by absolute offset (rather than concatenating super-k-mers per minimizer bucket, as in Blight) avoids the fixed 2(k-1)-bit tail cost per super-k-mer and greatly reduces space mechanism
- ★ SSHash's overall space for the human genome (k=31, m=20) can be tuned to 8.28 bits/k-mer or less, versus at least 11.50 bits/k-mer for a naive per-super-k-mer partitioning scheme finding
- ★ The dictionary is built as a combination of minimal perfect hashing (via PTHash) and compact Elias-Fano encodings, supporting Lookup, Access, and streaming membership queries in compressed space method
- ★ Existing k-mer dictionaries based on FM-index achieve compact space but slow practical query time, whereas hashing-based ones (Pufferfish, Blight) are fast but use more space; SSHash aims to balance both finding
- PTHash is used as the MPHF because it evaluates 2-4x faster than other MPHF techniques while retaining good space effectiveness resource
- C++ implementation of the dictionary (SSHash) is publicly available resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| k-mer dictionary space/statistics benchmarking (Table 2) | Cod genome de Bruijn graph paths, k=31 | none | number of k-mers (n), paths (p), bases (N) | sshash (C++), PTHash MPHF, Elias-Fano encoding |
| k-mer dictionary space/statistics benchmarking (Table 2) | Kestrel genome de Bruijn graph paths, k=31 | none | number of k-mers (n), paths (p), bases (N) | sshash (C++), PTHash MPHF, Elias-Fano encoding |
| k-mer dictionary space/statistics benchmarking (Table 2) | Human genome de Bruijn graph paths, k=31 | none | number of k-mers (n), paths (p), bases (N); overall dictionary space in bits/k-mer | sshash (C++), PTHash MPHF, Elias-Fano encoding |
| k-mer dictionary space/statistics benchmarking (Table 2) | Bacterial genome collection de Bruijn graph paths, k=31 | none | number of k-mers (n), paths (p), bases (N) | sshash (C++), PTHash MPHF, Elias-Fano encoding |
- ▼ SSHash achieves an overall dictionary space of 8.28 bits/k-mer (or less) for the human genome at k=31, m=20 8.28 bits/k-mer
- – A naive scheme that partitions strings per super-k-mer costs at least 11.50 bits/k-mer for the same human genome setting 11.50 bits/k-mer
- ▼ For k=31, m=20 there are approximately 6.5x fewer minimizers than k-mers ≈6.5-fold
- ▼ The human genome has about 396×10^6 super-k-mers versus about 2.5×10^9 distinct k-mers ≈6.3-fold fewer super-k-mers than k-mers
- count n=502,465,200; p=2,406,681; N=574,665,630 (Cod dataset statistics at k=31 (Table 2))
- count n=1,150,399,205; p=682,344; N=1,170,869,525 (Kestrel dataset statistics at k=31 (Table 2))
- count n=2,505,445,761; p=13,014,641; N=2,895,884,991 (Human dataset statistics at k=31 (Table 2))
- count n=5,350,807,438; p=26,449,008; N=6,144,277,678 (Bacterial dataset statistics at k=31 (Table 2))
- count z≈396×10^6 super-k-mers, n≈2.5×10^9 k-mers (Human genome, k=31, m=20)
- fold_change 8.28 bits/k-mer vs ≥11.50 bits/k-mer (SSHash overall space vs naive per-super-k-mer partitioning, human genome)
- other MPHF cost ≈3 bits per minimizer (theoretical minimum log2(e)≈1.44 bits/key) (space cost of minimal perfect hash function used for minimizers)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This is a computer science/bioinformatics paper describing a data structure (SSHash) for compressed, associative k-mer dictionaries, evaluated empirically on benchmark genomic datasets (Cod, Kestrel, Human, Bacterial) by measuring space usage (bits/k-mer) and query time. The paper does not use inferential statistical hypothesis testing; results are reported as descriptive performance measurements (space and time trade-offs) compared against prior data structures (Pufferfish, Blight, etc.), based on the provided text.
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The paper reports single deterministic performance figures (e.g., bits/k-mer, query counts) for each dataset without repeated trials or variability measures.↳ Could also: Reporting multiple runs with mean and standard deviation (or range) of timing measurements, especially for query throughput, would also be a standard practice — Timing benchmarks can vary run-to-run due to system/cache effects; reporting variability alongside point estimates helps readers gauge measurement stability, which is a common convention in systems/algorithms benchmarking papers.
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The paper compares its data structure's space and time performance against prior methods (e.g., Pufferfish, Blight) across a fixed set of benchmark datasets.↳ Could also: Including a broader or randomized sample of genomic datasets, or statistical comparison (e.g., paired tests across dataset replicates) of performance differences, could also be used — This would let readers assess whether observed space/time improvements generalize beyond the specific benchmark datasets chosen, which is a common consideration in systems benchmarking research.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
scope.md — pmid-35758794 (SSHash: Sparse and skew hashing of K-mers)
Paper: Pibiri GE, Sparse and skew hashing of K-mers, Bioinformatics 2022, btac245 (PMID 35758794, PMC9235479). Code: https://github.com/jermp/sshash (authors' own tool — single-author tool paper; P16 satisfied by the own repo).
What kind of paper this is
A data-structures / methods paper. There is no wet-lab. Every reported number is a pipeline-derived computational measurement: the space (bits per k-mer) and time (construction, query) of building & querying the SSHash k-mer dictionary on standard public genomes. This is an almost-ideal reproduction target: the inputs are public, the tool is open source, and the headline metric (bits/k-mer) is deterministic and hardware-independent.
The pipeline (per the paper Methods + repo scripts)
For each genome FASTA:
bcalm -kmer-size 31 -abundance-min 1→ maximal unitigs (compacted dBG).ust -k 31→ spectrum-preserving string set (path cover), gzipped.sshash build -k 31 -m <m>(regular) and--canonical-parsing(canonical).sshash bench→ reports total bits and bits/k-mer.sshash query(streaming) of a read set → hit rate + ns/k-mer. (Recipe pinned fromscript/download_and_preprocess_datasets.shandscript/benchmark_dictionary.shat tag v3.0.0, the paper-era release — v3.0.0 published 2022-07-01, the release the README's #datasets section links.)
Datasets the paper uses (4 genomes + 4 read sets), k=31 throughout
| name | organism | source (indexed genome) | #k-mers (Table 2) | streaming read set |
|---|---|---|---|---|
| Cod | Gadus morhua | Ensembl Gadus_morhua.gadMor3.0.dna.toplevel.fa.gz |
502,465,200 | SRR12858649 |
| Kestrel | Falco tinnunculus | Ensembl Falco_tinnunculus.FalTin1.0 |
1.15 B | SRR11449743 |
| Human | GRCh38 | Ensembl Homo_sapiens.GRCh38.dna.toplevel |
2.51 B | SRR5833294 |
| Bacterial | 8000+ genomes | Zenodo 995689 bacterial.genome.fixed.fa |
5.35 B | SRR5901135 |
This RU's named accession is SRR12858649 = the Cod read set. So the natural, faithful reproduction unit is the Cod dataset end-to-end: build the Cod index from gadMor3.0 unitigs and stream-query SRR12858649. Cod is also the smallest genome (~1.5 min build per paper), so it is the cheapest faithful 1:1 target.
IN SCOPE (pipeline-derived → attempted), Cod dataset, k=31
Deterministic, hardware-independent (gold 1:1 targets):
- C1 #k-mers in Cod index = 502,465,200 (Table 2).
- C2 SSHash regular space = 6.98 bits/k-mer (Table 4, m=17).
- C3 SSHash canonical space = 7.92 bits/k-mer (Table 4, m=16).
- C4 Streaming-query hit rate of SRR12858649 vs Cod index = 81.37% (Table 6a).
Hardware-dependent (reproduce the pipeline, compare order-of-magnitude only — paper used 1 core of an Intel i9-9940X @3.30 GHz, 128 GB RAM; «our HPC» CPU differs):
- C5 construction time regular = 1.5 min / canonical = 2.0 min (Table 7).
- C6 streaming ns/k-mer: regular 166 / canonical 111 (Table 6a).
Stretch (after Cod), same metrics on Kestrel / Human if compute budget allows — 80% is a floor not a ceiling.
OUT OF SCOPE (not attempted)
- Competing tools' numbers (Pufferfish, Blight) in Tables 4–6 — those are other tools; reproducing them is a separate, much larger effort and not needed to validate SSHash's own reported metrics. Noted, not attempted.
- Absolute wall-clock timings as 1:1 values — different CPU; compared only as same-order-of-magnitude sanity (C5/C6).
Reproduction strategy
- All compute on «our HPC». Downloads (genome, reads, repos) on front1 → «infra».
- Build sshash @v3.0.0, bcalm (bioconda), UST (source) in a conda env on «infra».
- BCALM2+UST+sshash build + bench + query run as a SLURM job (std partition).
- Cross-check option: the same author's LPHash Zenodo deposit (10.5281/zenodo.7239205)
ships
cod.k31.unitigs.fa.ust.fa.gz(157.7 MB) — the prebuilt UST cod SPSS; used as a fallback / independen
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Reproduction footprint
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